Handling imbalanced datasets is a challenging problem in Knowledge Discovery in Databases. Several associative classification approaches have been proposed in order to treat this kind of data. However, these approaches suffer from a major drawback which is the use of a unique single measure for the filtering and selection of rules. This could be misleading since each measure is constructed in order to select a specific category of rules. To overcome such drawback, we introduce, IARCID, a novel approach that consists in aggregating several measures during the rule selection phase using the fuzzy Choquet Integral in order to produce a global measure. This latter is shown to be suitable for the classification of Imbalanced datasets from different domains. The performance of IARCID is assessed on four datasets with reference to three evaluation metrics. Experimentations show that IARCID outperforms approaches which use one single measure for the rule selection, by offering good classification results with all the datasets and according to all the assessment measures used for